Inferring user equipment location data based on sector transition

ABSTRACT

Determining a location of a user equipment (UE) based on historical location data and historical sector transition data is disclosed. A correlation between historic location information and a historic sector transition can be determined. The correlation can be stored in a searchable data set. A location of a current UE can be inferred based on a sector transition of the current UE. The sector transition of the current UE can be searched against eh data set to indicate a likely location of the current UE based on historical information. The searchable data set can be based on sparse location data enabling location determinations for a current UE that can otherwise lack location services. Moreover, an order of a sector transition can imbue a directionality to stored location information such that a likely location in a sector can be correlated to a transition from a prior sector of a network session of the UE.

RELATED APPLICATION

The subject patent application is a continuation of, and claims priorityto, U.S. patent application Ser. No. 15/221,568, filed Jul. 27, 2016,and entitled “INFERRING USER EQUIPMENT LOCATION DATA BASED ON SECTORTRANSITION,” the entirety of which application is hereby incorporated byreference herein.

TECHNICAL FIELD

The disclosed subject matter relates to inferring a user equipmentlocation. More specifically, this disclosure relates to inferring a userequipment location based on a historical equipment density of a sectorcorrelated to a historical sector transition.

BACKGROUND

By way of brief background, a conventional location data service cangenerally provide a location associated with a sector of coverageprovided by a radio device of a radio access network (RAN). e.g., asector associated with a NodeB, eNodeB, etc. The conventional locationdata service can, in some instances, poorly represent the actuallocation of the user equipment (UE) being located. A convention locationdata service, for example where the UE does not provide more accuratelocation information via global positioning system (GPS), assisted GPS(aGPS), etc., can employ a ‘shorthand’ technique for indicating alocation of a UE. This shorthand can return a designated locationassociated with a service sector for the UE. Typical locationassociations for a conventional location data service can substitute, asexamples, the location of the radio device of the RAN, a centroid of theserving sector, or some other arbitrarily selected location associatedwith the serving sector, etc., as the location of a UE. Thus, forexample, a UE in a conventional system can be indicated as being locatedat the physical location of the radio device of the RAN even where it isactually located some distance from the radio device. It can be commonfor a UE to be reported as being as much as several kilometers from theUE's actual location in these conventional systems, e.g., where theradio device is several kilometers from the UE and the conventionalsystem nonetheless indicates, as ‘shorthand’ location data, that the UEis collocated with the radio device.

This can be particularly problematic in environments where UEs cannot,or otherwise do not, frequently provide other types of more accuratelocation data, e.g., in environments where UEs are generally notGPS/aGPS enabled, environments where UEs don't generally report accuratelocation information, etc. It can be noted that in environments whereaccurate location data, e.g., GPS, aGPS, etc., is not readily available,or perhaps even possible, that a conventional location data service canproviding less accurate location estimates based on the aforementionedconventional techniques. Even where some UEs can provide accuratelocation data, the accurate location data can often be sparse incomparison to the overall number of UEs employing the associatedwireless network in those areas. This sparsity can result from, amongother causes, a relatively low number of UEs providing accurate locationdata, UEs providing intermittent accurate location data, etc.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example system that can enable access toinferred location data based on historical session data and historicallocation data in accordance with aspects of the subject disclosure.

FIG. 2 is a depiction of an example system that can enable access toinferred location data based on historical session data, historicallocation data, and supplemental data, in accordance with aspects of thesubject disclosure.

FIG. 3 illustrates an example system that can enable access to inferredlocation data based on correlating historical session data, historicallocation data, and supplemental data, and hashing correlated dataagainst current UE sector transition information in accordance withaspects of the subject disclosure.

FIG. 4 illustrates an example system that can enable access to inferredlocation data based on historical session data, historical locationdata, and supplementary data, in accordance with aspects of the subjectdisclosure.

FIG. 5 illustrates an example method facilitating access to inferredlocation data based on historical session data and historical locationdata in accordance with aspects of the subject disclosure.

FIG. 6 illustrates an example method enabling access to inferredlocation data based on historical session data, historical locationdata, and supplementary data, in accordance with aspects of the subjectdisclosure.

FIG. 7 depicts an example method that can enable access to inferredlocation data based on satisfying a distance rule related to a distancebetween a radio device location and a historically dense location withina sector in accordance with aspects of the subject disclosure.

FIG. 8 illustrates an example method that, in response to receiving alocation query, can return inferred location data from searchable datathat is updated based on a correlation between historical session data,historical location data, and available supplementary data, inaccordance with aspects of the subject disclosure.

FIG. 9 depicts an example schematic block diagram of a computingenvironment with which the disclosed subject matter can interact.

FIG. 10 illustrates an example block diagram of a computing systemoperable to execute the disclosed systems and methods in accordance withan embodiment.

DETAILED DESCRIPTION

The subject disclosure is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the subject disclosure. It may be evident, however,that the subject disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing the subjectdisclosure.

Whereas a conventional location data service may merely provide alocation for a UE as a location generally associated with a servingsector, it can be desirable to provide more accurate location estimatesfor the UE. A serving sector can be a wireless network coverage area,generally known as a sector that is associated with providing wirelessnetwork coverage to a UE via a radio device of a RAN. A sector can beassociated with a radio device of the RAN, e.g., a radio device of aNodeB, eNodeB, etc. A serving sector can be a sector that is currentlyproviding wireless service to a UE. A non-serving, more commonly just‘sector,’ can be another sector not currently providing wireless serviceto the UE, although a non-serving sector from the perspective of a firstUE can be a serving sector from the perspective of another UE.

A conventional location data service, in some instances, can employ a‘shorthand’ or ‘shortcut’ technique that can typically provide locationdata for a UE that may poorly represent an actual location of the UE.This shorthand, in the absence of more accurate location data, cansimply return a designated location associated with a serving sector. Insome circumstances, a location for a UE can be substituted with alocation of a serving RAN radio device or some other arbitrarilyselected default-type location associated with the serving sector. Thus,for example, a UE in a conventional system can be indicated as beinglocated at the same physical location as the RAN radio device servingthe UE. This can be true of conventional location data servicetechniques even where the UE is actually located some distance from theRAN radio device. Differences between an actual location and anestimated location in a conventional system can be on the order of thesize of the sector itself, e.g., a UE in the sector can be reported asbeing located at the RAN radio device location even where it, inactually, can be located anywhere in the sector, including at the edgeof the sector. In an example, where a sector provides coverage out to 3kilometers, the location of the UE can be off by as much as about 3kilometers.

The lack of accuracy in conventional technologies can be particularlycommon in environments where UEs cannot, or otherwise do not, frequentlyprovide other types of more accurate location data, e.g., inenvironments where UEs are generally not GPS enabled, not aGPS enabled,environments where UEs don't generally report accurate locationinformation, etc. In some environments where accurate location data,e.g., GPS, aGPS, etc., is not readily available, or perhaps evenpossible, a conventional location data service can provide locationestimates based on the aforementioned conventional techniques. Evenwhere some UEs can provide accurate location data, the accurate locationdata can often be sparse in comparison to the overall number of UEsemploying the associated wireless network in those areas. This sparsitycan result from, among other causes, a relatively low number of UEsproviding accurate location data, UEs providing only intermittentaccurate location data, etc. As such, it can be desirable to improve theaccuracy of location data provided by a location data service incontrast to conventional techniques, more especially in environmentswith sparse accurate location reporting.

Providing location data for mobile devices in a wireless network, whereaccurate location data is sparse, can be difficult. UEs, e.g., mobiledevices, etc., can report accurate location data infrequently to thewireless network, only some UEs may regularly report accurate locationdata to the wireless network while other UEs don't report at all or onlyreport infrequently, etc., resulting in sparse location data for anygiven sector. Leveraging sparsely reported accurate location data canenable inferring location data for UEs, e.g., between reports ofaccurate location data, for UEs that cannot or do not report accuratelocation data, etc. Identifying a likely location for a UE in a wirelessnetwork sector can be based on identifying a densely populated location,e.g., a dense bin, etc., in the sector. An inference of a UE locationcan therefore be based on an inference related to a dense bin of asector. Moreover, a transition between sectors can be employed to infera UE location based on a correlation between a dense bin and ahistorical sector transition. This can result in location data that canmore accurately reflect the location of a UE than simply using alocation of associated with a radio device for a sector, e.g., ratherthan reporting a default-type location for a sector, the reportedlocation can reflect areas that have a historically higher probabilityof being populated for a given transition between sectors, etc. As anexample, northbound traffic from a first sector to a second sector canhistorically be associated with congregating at a shopping mall alongthe East side of a road, while southbound traffic, transitioning from athird sector to the first sector, can be associated with congregating atanother location, such as where there may be no exit from the southboundroad to the shopping mall. Thus, in the example, where a UE isdetermined to transition from the first to the second sector, indicatingnorthbound travel, can result in inferring that the most likely locationof the UE is at the shopping mall. Moreover, in this example, where theUE is determined to traverse from the third sector to the first sector,indicating southbound travel, the most likely location can be the otherlocation. It will be noted, in this example, that where a radio deviceof the wireless network is located some distance from the shopping mallor the other location, the location of the shopping mall or the otherlocation can be a more accurate location than simply using the locationof the radio device as a default location for all devices in the firstsector as can be reported in a conventional system.

The term historical sector transition, as used herein, generallyconnotes sector transitions that have occurred in a first time periodending prior to a second time period ending, e.g., a sector transitionthat occurred last week can be considered a historical sector transitionin view of a sector transition that occurred two days ago, two minutesago, one second ago, etc. Similarly, the term historical location datagenerally refers to location data from a time period that occurs beforeanother time period, e.g., location data from a week ago can beconsidered historic location data in view of location data from two daysago, two minutes ago, one second ago, etc. Conversely, the term‘current’ or ‘instant’ as applied to location data, sector transition,session data, etc., generally connotes occurrences/data of a second timeperiod ending after an ending of a first earlier time period. Of note, asector transition that occurs after a historical sector transition canbe a ‘current sector transition’ even where the ‘current sectortransition’ can occur in the past, e.g., a sector transition recordedtwo weeks ago can be termed a ‘current sector transition’ where itcorresponds to a historical sector transition that ended three weeks inthe past, three months in the past, one year in the past, etc. In someinstances, a ‘current sector transition’ can occur in the immediatepast, e.g., seconds, minutes, hours, etc., in the past and in relationto a historical sector transition that ended prior to the time of the‘current sector transition’, such that a ‘current sector transition’that occurred four minutes in the past is current in relation to ahistorical sector transition that occurred more than four minutes in thepast.

The density analysis and correlation of historical sector transitionscan adapt to reflect changes in population density, e.g., the dense bincan shift to a different location as the historical data reflectschanges in where UEs congregate. As such, the dense bin can reflectchanges in where UEs congregate, an aspect is not readily possible inconventional techniques, e.g., a radio device location is typicallyfixed and does not change with changes in UE location over time. Tocontinue the previous example, where a new exit is built providingsouthbound traffic access to the shopping mall, historical sectortransitions for southbound UEs can be associated with increasing densityat the shopping mall location such that transitions from the first tosecond sector and transitions from the third to first sector can both beassociated with a dense bin at the location of the shopping mall. Thisis again, typically more accurate than the location of the radio devicethat is located a distance from the shopping mall.

In some embodiments, a most likely location point in a coverage patterncan be estimated using a kernel density estimation (KDE) technique toprovide an estimate of most frequented locations in a coverage area. Fora sector, collected location data can be grouped/binned to a closestgeographic coordinate system, e.g., a bin. In some embodiments, databinning can also comprise data processing to reduce effects of minorobservation errors. The location data values can be assigned to a bin,e.g., a determined geographic interval, as a form of quantization of thelocation values. A ‘bin’ can be any arbitrary size and/or shape, but canoften be represented as a simple grid pattern. As an example, themilitary grid reference system (MGRS). MGRS bins can be considered astandard way of binning location data, e.g., latitude and longitude,with arbitrary precision. MGRS can be the geo-coordinate standard usedby some militaries for locating points on the earth. The MGRS ishistorically derived from the universal transverse mercator (UTM) gridsystem and the universal polar stereographic (UPS) grid system, buttypically uses a different labeling convention. It will be noted thatother techniques, e.g., Gaussian estimators, etc., for determining areasof UE density in a given sector can be employed without departing fromthe scope of the presently disclosed subject matter. The granularity ofthe bin coverage can be selected to reflect parameter(s) relevant to anenvironment, e.g., in an urban environment, a finer granularity, e.g., asmaller bin size, can be employed in contrast to a larger bin size,e.g., a coarser granularity, in a rural/agrarian environment.

In a further aspect, supplemental data can also be consumed to enhancean accuracy of inferred location data. In some embodiments, supplementaldata can be a source of accurate location data, sometimes more accuratethan GPS/aGPS, etc. As an example, a network operator can receive accesspoint location data that can be more accurate than GPS type locationdata, which information can be employed to hyper-accurately locate a UE.In a further example, user inputs can be employed to more accuratelydetermine a UE location, e.g., where a user does an internet search fora particular destination, this information can be used to supplement GPSlocation data and provide hyper-accurate location information. As such,the supplemental data can improve correlations between sectortransitions and location data in a given sector. Supplemental data cancomprise calendar information associated with a user profile of a UE,social media information associated with a user profile of a UE,access/handshake events for wireless connections associated with a UE,billing addresses associated with a UE, home addresses associated with aUE, work addresses associated with a UE, road/route map data, tetheringevents between a first UE a second UE allowing the location of thesecond UE to be affiliated with the location of the first UE, paymentfor services such as tolls, parking, movie tickets, etc., associatedwith a user profile affiliated with a UE, etc., such that nearly anytype of event affiliated with the UE that can be associated with anaccurate location can be employed in correlating a sector transitionwith a historical location of a UE to enable determining/updating adense bin that can be employed in inferring a location of a current UEgiven a sector transition.

In some embodiments, a distance between a dense bin and a radio devicelocation can be determined. Where the distance is determined to satisfya rule related to a selectable distance between the radio and the bin,e.g., the distance is sufficiently large, has a small deviation, etc.,the location of the dense bin can be employed. Similarly, where the ruleis not satisfied, e.g., the distance is not sufficiently large, has alarge deviation, etc., the location of the radio device can be employed.In an aspect, this can be useful where the location of the radio devicecan be known with a high degree of precision and, as such, can be adesirable location to use where the determined distance from a dense bincan be suspected of being inaccurate, e.g., large deviations in locationinformation being assigned to the dense bin, error, etc., or where thedense bin is simply located so close to the radio that using the radiolocation is deemed to be sufficiently accurate. Similarly, the locationof the radio device can be employed where the bin density does notsatisfy other rules, such as but not limited to, the bin density beingderived from a statistically significant number of reporting UEs, areasof known radio interference, location data corruption, session datacorruption, GPS/aGPS reporting errors, etc. In this aspect, where thehistorical session data, historical location data, supplemental data, orcombinations thereof are compromised sufficiently, e.g., satisfying arule related thereto, then it can be determined that an inference as toa current UE location can be correspondingly suspect and can failover tousing a location of a radio of the service sector.

It is also noted that inferred location by the presently disclosedsubject matter is typically more accurate than simply providing theradio device location according to conventional techniques. However, theinferred location is typically not as accurate as location data reportedvia GPS, aGPS, etc. Despite this, the presently disclosed subject mattercan provide valuable location information in environments where there issparse accurate location data. As an example, where historically someUEs transitioning into a first sector from a second sector report afirst location, an inference can be formed that when a current UEtransitions from the second to the first sector that the current UE willalso be located at the first location. However, in this example, wherethe current UE is actually located at a second location, the inferencecan be found to be incorrect. Despite this incorrect information givenfor an individual current UE, a location of a current UE can still bemore accurate than no location data at all, reporting the location asthe same as a serving sector radio device, etc., given statisticallysignificant historical location data and sector transition data.Moreover, with increasing sample size for historical location datacorrelated to the example transition from the second sector the firstsector, there, in fact, can be an increase accurately inferring a UElocation, on the whole, despite outlier individual UE locationmisrepresentations. Additionally, where binning is adapted to result inhigh contrast between bin-density levels for adjoining bins, accuracy ininferences can also be improved, e.g., where adjoining bins show similardensity there can be a lower probability of a current UE being in anyone particular bin as compared to adjoining bins having sharplydifferent densities. Similarly, a frequency of updating bin densitiescan be associated with a level of accuracy. As such, the parameter(s) ofthe disclosed subject matter can be adapted to fit a particularenvironment or determined analytical goal without departing from thescope of the current disclosure. It will be noted that where all UEs arecapable of accurate location information, e.g., GPS, etc., at all times,there is likely to be a lesser need for the disclosed subject matter,however, in real world environments with large populations of UEs, for agiven number of accurate location reporting UEs, the disclosed subjectmatter can provide statistically relevant location information fornon-reporting UEs that can be more accurate than conventionaltechniques.

To the accomplishment of the foregoing and related ends, the disclosedsubject matter, then, comprises one or more of the features hereinaftermore fully described. The following description and the annexed drawingsset forth in detail certain illustrative aspects of the subject matter.However, these aspects are indicative of but a few of the various waysin which the principles of the subject matter can be employed. Otheraspects, advantages and novel features of the disclosed subject matterwill become apparent from the following detailed description whenconsidered in conjunction with the provided drawings.

FIG. 1 is an illustration of a system 100, which can enable access toinferred location data 122 based on historical session data 130 andhistorical location data 140 in accordance with aspects of the subjectdisclosure. System 100 can comprise location inference component (LIC)110. LIC 110 can receive historical session data 130. Historical sessiondata 130 can comprise information about historical UE transitionsbetween sectors. As an example, where a UE session is in progress,employing a radio device of a first sector while moving into an areaserved by a second sector, the communicative link can be transferredfrom the radio device of the first sector to a radio device of thesecond sector, e.g., a handover between sectors that is well understoodin the relevant art.

LIC 110 can further receive historical location data 140. Historicallocation data can comprise accurate location data, e.g., location datadetermined via GPS, aGPS, etc., for a UE. The historical location datacan be employed to determine a location of high UE population density,e.g., a dense bin. The dense bin can represent an area of a sectorhaving a higher density of accurate location data UEs reporting, e.g.,an area of the sector where there is a higher density of reporting UEsthan other areas of the sector. This can suggest that UEs, bothreporting and non-reporting, generally are located in the arearepresented by the dense bin.

Historical location data 140 can be correlated to historical sessiondata 130 by LIC 110. For a wireless network environment having a numberof UEs, some of which report accurate location data, the correlation ofhistorical location data to sector transition data can result in a dataset that can suggest a relationship between directionality, as isrelated to the order of sector transitions, and a dense bin. In anaspect, a UE historically transitioning sectors in a first order can beassociated with the same, or a different, dense bin than a UEhistorically transitioning sectors in a second order. As an example, aUE that transitions sectors while traversing a route from A to B can beassociated with a first dense bin, while a UE traversing sectors on aroute from B to A can be associated with a second dense bin. In anotherexample, a UE that transitions sectors while traversing a route from Ato B can be associated with a first dense bin, while a UE traversingsectors on a route from B to A can be associated with the first densebin. As such, the directionality or order of sector transitions can beindividually correlated with a dense bin that can be the same ordifferent and can reflect the environment in which the UE is operating,e.g., route features, user propensities, etc.

The correlation of historical location data and historical sector datacan be employed in inferring a location of a current UE. LIC 110 canreceive UE session data 120. UE session data 120 can comprise sectortransition information for a current UE. The sector transitioninformation can be employed to search a data set comprisingcorrelation(s) between historical location data and historical sessiondata. As an example, where a current UE transitions sectors associatedwith a route from A to B, and the data set comprises a correlationbetween historical sector transitions on the route from A to B with afirst dense bin, it can be inferred that the current UE location iswithin the area associated with the first dense bin. Likewise, where thecurrent UE traverses sectors from B to A and the historical correlationof B to A is associated with a second dense bin, the inference that thecurrent UE is located within the second dense bin can be determined. Assuch, LIC 110 can enable access to inferred location data 122. In thebounds of the current example, inferred location data 122 can compriselocation data for the first dense bin where the sector transitions arefrom A to B, or can comprise location data for the second dense binwhere the sector transitions are from B to A. Moreover, in someinstances, the example first and second dense bin can be the same densebin representing the same area, or the example first and second densebin can be different dense bins representing different areas.

In some embodiments, the sector transitions can be limited to onetransition from a first sector to a second sector. Where a routecomprises a plurality of sector transitions, for example from sectorA→B→C→D, a first correlation can be between the historical location datain sector B and the corresponding transition from sector A→B, a secondcorrelation can be between the historical location data in sector C andthe corresponding transition from sector B→C, and a third correlationcan be between the historical location data in sector D and thecorresponding transition from sector C→D. Parsing of the historicallocation and session data can be included in a data set and can reflecthistorical conditions/behaviors for transitions between each sector or aroute comprising a plurality of sector transitions. For a current UEtransitioning from sector A→D, these embodiments can form an inferencethat the current UE is at a dense bin of D only for transitions fromsector C. As an example, where a wireless network environment supports,for example, one million UEs, only some of the UEs are GPS enabled, andonly some of the GPS enabled UEs have reported location data in the pastseven days, this information can be correlated to historical sessiondata to form inferences about the location of UEs in the wirelessnetwork environment. Continuing the example, where in the last sevendays, 2,000 transitions from sector A→B of the wireless networkenvironment report accurate location information, 92% can report at binR8 of sector B, 3% can report at bin R9 of sector B, and the remaining5% can report at other bins of the sector B. As such, in this example,there can be a strong correlation between sector transitions from A→B asbeing located at bin R8. Where, in the example, another UE transitionsfrom sector A→B, an inference can be made based on the thousands ofhistorical data points that the current UE will likely be located in R8of sector B. Moreover, in this example, where bin R8 geographicallyincludes a popular restaurant next to a lake, this supplementaryinformation can be employed to infer that the current UE is located atthe restaurant within bin R8 rather than in the wet portion of bin R8.It is noted that the specific numbers and parameters of the presentexample are arbitrarily selected for illustrative purposes only and arenot intended to limit the disclosure in any way.

In some embodiments, the data set can further comprise segments fromother routes, such as other historical data for correspondingtransitions from sector D→C, other historical data for A→B, otherhistorical data for B→C, etc. Moreover, In some embodiments, such aswhere sector overlap allows, some segments can be included in the dataset, such as other historical data for A→D, indicating that a transitioncan be by a first route from A→B→D (e.g., never transitioning betweenB→C) and another route can be from A→D directly. For a current UEtransitioning from sector A→D, these embodiments can be employed to forman inference that the current UE is at a first dense bin of D only fortransitions from sector C→D, a second dense bin of D only fortransitions from sector B→D, and a third dense bin of D only fortransitions from sector A→D. Of note, combinations of the first, second,and third dense bin of D can be the same or different.

In some embodiments, the sector transitions can comprise a plurality oftransitions from a starting sector to an ending sector. As before, wherea route comprises a plurality of sector transitions, for example fromsector A→B→C→D, a first correlation can be between the historicallocation data in sector D and the transition from sector A (the startingsector) to D (the ending sector). This can reflect that UEs starting inA and ending in D are typically associated with a dense bin in D andthat the transition across sectors B and C is inherently convolved inthe sector pair “A→D”. In an aspect, where other routes between sector Aand sector D are also represented, for example A→B→F→D, then the densebin in D can reflect behaviors associated with UEs starting at A andending at D for both routes, e.g., both A→B→C→D and A→B→F→D. For acurrent UE transitioning then from sector A→D, regardless of route,these embodiments can form an inference that the current UE is at thedense bin of D.

In an aspect, the embodiments disclosed herein can be combined. As such,in some embodiments, some areas served by some sectors can employ parsedroute sector transitions and other areas served by other sectors canemploy start to end sector transitions, etc. Moreover, in someembodiments, both parsed route and start to end sector transitions canbe performed concurrently allowing for inferences based on either orboth. In these embodiments, selection of a likely location can be basedon another criterion, for example, a history of the current UE, etc.

FIG. 2 is a depiction of a system 200 that can enable access to inferredlocation data 222 based on historical session data 230, historicallocation data 240, and supplemental data 250, in accordance with aspectsof the subject disclosure. System 200 can comprise LIC 210. LIC 210 canreceive historical session data 230. Historical session data 230 cancomprise information about historical UE transitions between sectors,e.g., a handover event between sectors of a communication session for aUE.

LIC 210 can further receive historical location data 240. Historicallocation data can comprise accurate location data, e.g., location datadetermined via GPS, aGPS, etc., for a UE. The historical location datacan be employed to determine a location of high UE population density,e.g., a dense bin. The dense bin can represent an area of a sectorhaving a higher density of UEs reporting accurate location data, e.g.,an area of the sector where there is a higher density of reporting UEsthan other areas of the sector. This can suggest that UEs, bothreporting and non-reporting, are typically located in the area of thesector represented by the dense bin.

LIC 210 can receive supplemental data 250. Supplemental data can be dataother than accurate location data, e.g., other than GPS data, aGPS data,etc. In an aspect, supplemental data can be employed to determinelocation data and, in some instances, hyper-accurate location data. Asan example, where a UE is provided to an employee and is carried by theemployee to work, when the UE enters a sector serving the areaassociated with the entrance to the employer's campus, the billingaddress, e.g., the employer's address, can be sourced as supplementaldata to hyper-accurately determine that the UE is at the entrance to theemployer's campus, which, in some instances, can be more accurate thanGPS location information. In another example, where a UE user uses awireless carrier application that allows geotagging, e.g., identifyingwhen the user is at a restaurant, movie theatre, gym, etc., a user inputgeotag can be employed as hyper-accurate location information.Similarly, use of social media applications can provide access tosimilar geotagging data. As a further example, use of an access point(AP) by a UE can be associated with generating supplemental data thatcan indicate that the UE is within a determined distance of the AP. Asanother example, searching for a destination performed on the UE cansupply both location and intended route information as supplemental datathat can be employed in inferring a location in a sector and correlatingthat location to session data. Numerous other examples of supplementaldata can be readily appreciated and all are considered within the scopeof the current disclosure even where not explicitly recited herein forthe sake of clarity and brevity.

Historical location data 240 and supplemental data 250 can be correlatedto historical session data 230 by LIC 210. The correlation(s) can beembodied in a data set. The data set can be stored local to LIC 210 orremote from LIC 210. For a wireless network environment having aplurality of UEs, some of which report accurate location data, thecorrelation of historical location data 240 and supplemental data 250 tosector transition data comprised in historical session data 230 canresult in a data set that can suggest a relationship betweendirectionality, via the order of sector transitions, and a dense bin ofa sector. In an aspect, a UE historically transitioning sectors in afirst order can be associated with the same, or a different, dense binthan a UE historically transitioning sectors in a second order. As such,the directionality or order of sector transitions can be individuallycorrelated with a dense bin that can be the same or different and canreflect the environment in which the UE is operating, e.g., routefeatures, user behaviors, etc.

The correlation of historical location data 240 and supplemental data250 with historical sector data can be employed in inferring a locationof a current UE. LIC 210 can receive UE session data 220. UE sessiondata 220 can comprise current sector transition information for acurrent UE. The current sector transition information can be employed tosearch a data set comprising correlation(s) between historical locationdata 240, supplemental data 250, and historical session data 230. As anexample, where a current UE transitions sectors associated with a routefrom A to B, and the data set comprises a correlation between historicalsector transitions on the route from A to B with a first dense bin, itcan be inferred that the current UE location is within the areaassociated with the first dense bin. The first dense bin can bedetermined via analysis of historical location data 240 and supplementaldata 250. Likewise, where the current UE traverses sectors from B to Aand the historical correlation of B to A is associated with a seconddense bin, the inference that the current UE is located within thesecond dense bin can be determined. As such, LIC 210 can enable accessto inferred location data 222.

In some embodiments, the sector transitions can be limited to onetransition from a starting sector to an ending sector, e.g., a first(starting) sector to a second (ending) sector. Where a route comprises aplurality of sector transitions, for example from sector A→B→C→D, aplurality of correlations can be determined, e.g., for sector A→B and adense bin in B, for B→C and a dense bin in C, and for C→D and a densebin in D. Parsing of the historical location and session data can beincluded in the data set and can reflect historical conditions/behaviorsfor transitions between each sector of a route comprising a plurality ofsector transitions. For a current UE transitioning from sector A→D,these embodiments can form an inference that the current UE is at adense bin of the ending sector, e.g., sector D, only for transitionsfrom the starting sector, e.g., sector C. In some embodiments, the dataset can further comprise segments from other routes.

In some embodiments, the sector transitions can comprise a plurality oftransitions from a starting sector to an ending sector. As before, wherea route comprises a plurality of sector transitions, for example fromsector A→B→C→D, a first correlation can be between a dense bin of sectorD and the transition from the starting sector, e.g., sector A, to theending sector, e.g., sector D. This can reflect that UEs starting in Aand ending in D are typically associated with the dense bin in D andthat the transition across sectors B and C is inherently convolved inthe sector pair “A→D”. Other routes between sector A and sector D can berepresented in the same sector pair “A→D,” for example A→B→F→D. As such,the dense bin in D can reflect behaviors associated with UEs starting atA and ending at D for both routes, e.g., both A→B→C→D and A→B→F→D. Thus,a current UE transitioning then from sector A→D, regardless of route,can be employed to infer that the current UE is at the dense bin of D.In an aspect, the embodiments disclosed herein can be combined.

FIG. 3 illustrates a system 300 that can enable access to inferredlocation data 322 based on correlating historical session data 330,historical location data 340, and supplemental data 350, and hashingcorrelated data against current UE sector transition information inaccordance with aspects of the subject disclosure. System 300 cancomprise LIC 310. LIC 310 can receive historical session data 330.Historical session data 330 can comprise information about historical UEtransitions between sectors, e.g., a handover event between sectors of acommunication session for a UE. LIC 310 can further receive historicallocation data 340. Historical location data can comprise accuratelocation data, e.g., location data determined via GPS, aGPS, etc., for aUE. The historical location data can be employed to determine a locationof high UE population density, e.g., a dense bin. The dense bin canrepresent an area of a sector having a higher density of UEs reportingaccurate location data, e.g., an area of the sector where there is ahigher density of reporting UEs than other areas of the sector. This cansuggest that UEs, both reporting and non-reporting, are typicallylocated in the area of the sector represented by the dense bin. LIC 310can further receive supplemental data 350. Supplemental data can be dataother than accurate location data, e.g., other than GPS data, aGPS data,etc. In an aspect, supplemental data can be employed to determinelocation data and, in some instances, hyper-accurate location data.

Historical location data 340 and supplemental data 350 can be correlatedto historical session data 330 by LIC 310 via data analysis component314. The correlation(s) can be embodied in a data set. The data set canbe updated by data organization component 316 and stored local to LIC310 or remote from LIC 310. In an aspect, data analysis component 314can determine a dense bin for a sector, for a given a historical sectortransition, based on historical location data 340 and any availablesupplemental data 350. Data analysis component 314 can determine a firstdense bin, for example, by employing a KDE technique, a Gaussianestimator, etc., associated with determining areas of UE density in agiven sector.

Data organization component 316 can structure the correlation of thehistorical sector transition(s) and dense bin(s) into a data set thatcan be searched, hashed, etc. Correlation of historical location data340 and supplemental data 350 to sector transition data comprised inhistorical session data 330 can suggest a relationship between the orderof sector transitions and a dense bin of a sector. In an aspect, a UEhistorically transitioning sectors in a first order can be associatedwith the same, or a different, dense bin than a UE historicallytransitioning sectors in a second order. As such, the directionality ororder of sector transitions can be individually correlated with a densebin that can be the same or different and can reflect the environment inwhich the UE is operating, e.g., route features, user behaviors, etc.,which can be reflected in the organization of the data set by dataorganization component 316.

The correlation of historical location data 340 and supplemental data350 with historical sector data can be employed in inferring a locationof a current UE. LIC 310 can receive UE session data 320. UE sessiondata 320 can comprise current sector transition information for acurrent UE. Sector parse component 324 can parse current sectortransition information from UE session data 320. The current sectortransition information can be employed to search a data set structuredby data organization component 316. The data set can comprisecorrelation(s) between historical location data 340, supplemental data350, and historical session data 330. In an aspect, the current sectortransition information parsed from UE session data 320 can be used tosearch, hash, or traverse the data set to access dense bin informationassociated with historical data that can be the same as, or similar to,the current sector transition information. In an aspect, this can beperformed by hash component 312. Hash component 312 can hash the currentsector transition information against the data set to return dense bindata associated with an area of the sector that, under the same/similarsector transitions, can be historically densely populated by locationreporting UEs. As an example, where a current UE transitions sectorsassociated with a route from A to B, and the data set comprises acorrelation between historical sector transitions on the route from A toB with a first dense bin, hashing the current UE transitions sectors canreturn first dense bin information that can be employed to infer thatthe current UE location is within the area associated with the firstdense bin. Likewise, where the current UE traverses sectors from B to Aand the historical correlation of B to A is associated with a seconddense bin, the hash can return second dense bin data that can beemployed in forming an inference that the current UE is located withinthe second dense bin. As such, LIC 310 can enable access to inferredlocation data 322.

FIG. 4 illustrates a system 400 that can enable access to inferredlocation data based on historical session data, historical locationdata, and supplementary data, in accordance with aspects of the subjectdisclosure. System 400 can comprise Radio-A, B, C, and D, that can becorrespondingly associated with sector-A, B, C, and D. Sectors A throughD can provide wireless service coverage to route-1, 2, and 3 asillustrated.

As illustrated, the physical locations of Radio A through D can bedifferent from the locations of dense UE populations 410-418. In someembodiments, dense UE populations 410-418 can be dense bins. Moreover,as examples, dense UE population 410 can be associated with a transitionfrom sectors B→A, dense UE population 412 can be associated with atransition from sectors A→B and from sectors C→B, dense UE population414 can be associated with a transition from sectors D→B, dense UEpopulation 416 can be associated with a transition from sectors B→C andfrom sectors D→C, and dense UE population 418 can be associated with atransition from sectors B→D and from sectors C→D.

A location inference component, not illustrated for clarity, candetermine dense UE populations 410-418 and correlate them to sectortransitions. A current UE, for example, can be traversing route-1 fromsector A→B. The current UE can provide UE session data to the LIC, whichcan parse the sector transition data from the UE session data, e.g.,sector transition from A→B can be parsed from the UE session data by theLIC. The LIC can then determine inferred location data for the currentUE, e.g., that the current UE is likely at location of dense UEpopulation 412, e.g., a dense bin, based on historical data indicatingthat transitions from sector A→B are correlated to accurate locationreporting by UEs having a high population density at 412.

Similarly, for example, a current UE traversing route-1 from sector B→Acan cause the LIC to return different inferred location data for thecurrent UE, e.g., that the current UE is likely at location of dense UEpopulation 410 based on historical data indicating that transitions fromsector B→A are correlated to accurate location reporting by UEs having ahigh population density at 410. As another example, a current UEtraversing route-1 and 3 from sector D→C can cause the LIC to returninferred location data that the current UE is likely at location ofdense UE population 416.

Sector-B illustrates two example locations of dense UE population, e.g.,412 and 414. Of interest, these two example locations of dense UEpopulation can be separately associated with different orders of sectortransitioning. As an example, transitions from sectors A→B and fromsectors C→B can both be correlated to dense bin 412, while transitionsfrom sector D→B can be correlated to dense bin 414.

Sector-D illustrates one example location of dense UE population, e.g.,418. Of interest, this one example location of dense UE population canbe associated with a plurality of different orders of sectortransitioning. As an example, transitions from sectors A→D, sectorsA→B→D, sectors B→D, sectors C→B→D, and sectors C→D can all be correlatedto dense bin 418. In an aspect, where route-3 serves all UE movementinto sector-D, location of dense UE population 418 can be correlated toall transitions from a starting sector to ending sector D. Of note,numerous other examples of session transitions can be illustrated forsystem 400, all of which are to be considered part of the currentdisclosure despite not being explicitly and exhaustively recited for thesake of clarity and brevity.

In view of the example system(s) described above, example method(s) thatcan be implemented in accordance with the disclosed subject matter canbe better appreciated with reference to flowcharts in FIG. 5-FIG. 8. Forpurposes of simplicity of explanation, example methods disclosed hereinare presented and described as a series of acts; however, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of acts, as some acts may occur in different ordersand/or concurrently with other acts from that shown and describedherein. For example, one or more example methods disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a describedexample method in accordance with the subject specification. Furtheryet, two or more of the disclosed example methods can be implemented incombination with each other, to accomplish one or more aspects hereindescribed. It should be further appreciated that the example methodsdisclosed throughout the subject specification are capable of beingstored on an article of manufacture (e.g., a computer-readable medium)to allow transporting and transferring such methods to computers forexecution, and thus implementation, by a processor or for storage in amemory.

FIG. 5 illustrates a method 500 that enables access to inferred locationdata based on historical session data and historical location data inaccordance with aspects of the subject disclosure. Method 500 canprovide location data for mobile devices in a wireless network byleveraging sparse accurate location data. In an aspect, UEs can reportaccurate location data infrequently to the wireless network. In anotheraspect, only some UEs may regularly report accurate location data to thewireless network while other UEs do not report at all or only reportinfrequently. These aspects can result in sparse location data for UEsin any given sector. Leveraging sparse accurate location data can enableinferences of location for UEs. Identifying a likely location for a UEin a wireless network sector can be based on identifying a denselypopulated location, e.g., a dense bin, etc., in the sector. An inferenceof a UE location can therefore be based on a location represented by adense bin of a sector. Moreover, a transition between sectors can beemployed in inferring a UE location based on a correlation between adense bin and a historical sector transition. This can result inlocation data that can more accurately reflect the location of a UE thansimply using a default location associated with a sector. Moreover, thedensity analysis and correlation to historical sector transitions canadapt to reflect changes in UE population density. As such, the densebin can reflect changes in where UEs congregate unlike conventionaltechniques.

At 510, method 500 can receive historical session data and historicallocation data. Historical location data can comprise accurate locationdata, e.g., location data determined via GPS, aGPS, etc., for a UE. Thehistorical location data can be employed to determine a location of highUE population density, e.g., a dense bin. The dense bin can represent anarea of a sector having a higher density of accurate location data UEsreporting, e.g., an area of the sector where there is a higher densityof reporting UEs than other areas of the sector. This can suggest thatUEs, both reporting and non-reporting, generally are located in the arearepresented by the dense bin. Historical session data can compriseinformation indicating a sector transition(s) as a UE moves along aroute. As an example, a UE that transitions sectors while traversing aroute from A to B can be associated with a first dense bin, while a UEtraversing sectors on a route from B to A can be associated with asecond dense bin.

At 520, method 500 can comprise, correlating the historical location andsession data. For a wireless network environment having a number of UEs,some of which report accurate location data, the correlation ofhistorical location data to sector transition data can result in a dataset that can suggest a relationship between directionality, as isrelated to the order of sector transitions, and a dense bin. In anaspect, a UE historically transitioning sectors in a first order can beassociated with the same, or a different, dense bin than a UEhistorically transitioning sectors in a second order. As such, thedirectionality or order of sector transitions can be individuallycorrelated with a dense bin that can be the same or different thancorrelations to a dense bin for other sector transitions and can reflectthe environment in which the UE is operating, e.g., route features, userpropensities, etc. Method 500, at 530, can comprise receiving currentsession data. Current session data can comprise sector transition(s) fora current UE.

At 540, method 500 can comprise inferring current location informationbased on the current session data and a correlation between thehistorical session and location data. Sector transition information forthe current UE can be employed to search a data set comprising acorrelation(s) between historical location data and historical sessiondata. As an example, where a current UE transitions sectors associatedwith a route from A to B, and the data set comprises a correlationbetween historical sector transitions on the route from A to B with afirst dense bin, it can be inferred that the current UE location iswithin the area associated with the first dense bin. As such, method 500can enable access to inferred location data, which can be returned inresponse to a query as shown at 550 of method 500. Inferred locationdata can comprise location information for a first dense bin based onthe current sector transition and a correlation between the first densebin, determined from historical location data, and historical sessiondata. At this point, method 500 can end.

In some embodiments, sector transitions can be limited to single hoptransitions from a first sector to a second sector. Where a routecomprises a plurality of sector transitions, for example from sectorA→B→C→D, each sector hop can be correlated to a dense bin based on theorder of the sector transition. Parsing of multiple hop sectortransitions into single hop segments can reflect historicalconditions/behaviors for transitions between each sector of a routecomprising a plurality of sector transitions. In some embodiments, thedata set can further comprise single hop segments from other routes,such as other historical data for transitions from sector D→C, otherhistorical data for A→B, other historical data for B→C, etc. Moreover,in some embodiments, such as where sector overlap allows, other routesegments can be included in the data set, such as other historical datafor A→D, indicating that a transition can be by a first route from A→B→Dand another route can be from A→D directly. For a current UEtransitioning from sector A→D, these embodiments can be employed to forman inference that the current UE is at a first dense bin of D only fortransitions from sector C→D, a second dense bin of D only fortransitions from sector B→D, and a third dense bin of D for transitionsfrom sector A→D. Of note, combinations of the first, second, and thirddense bin of D can be the same or different.

In some embodiments, the sector transitions can comprise a plurality oftransitions from a starting sector to an ending sector. As before, wherea route comprises a plurality of sector transitions, for example fromsector A→B→C→D, a correlation between the historical location data insector D and the transition from sector A (the starting sector) to D(the ending sector) can be determined. This determined correlation canreflect that UEs starting in A and ending in D can typically beassociated with a dense bin in D and that the transition across sectorsB and C can be inherently convolved in the sector transition pair “A→D”.In an aspect, where other routes between sector A and sector D are alsorepresented, for example A→B→F→D, then the dense bin in D can reflectbehaviors associated with UEs starting at sector A and ending at sectorD for multiple routes, e.g., both A→B→C→D and A→B→F→D. For a current UEtransitioning then from sector A→D, these embodiments can form aninference that the current UE is at the dense bin of D regardless of thespecific route between starting sector A and ending at sector D.

FIG. 6 illustrates a method 600, which enables access to inferredlocation data based on historical session data, historical locationdata, and supplementary data, in accordance with aspects of the subjectdisclosure. Method 600 can provide location data for mobile devices in awireless network by leveraging sparse accurate location data. Leveragingsparse accurate location data can enable inferences of location for UEs.Identifying a likely location for a UE in a wireless network sector canbe based on identifying a densely populated location, e.g., a dense bin,etc., in the sector. An inference of a UE location can therefore bebased on a location represented by a dense bin of a sector. Moreover, atransition between sectors can be employed in inferring a UE locationbased on a correlation between a dense bin and a historical sectortransition.

At 610, method 600 can receive historical session data, historicallocation data, and supplemental data. Historical location data cancomprise accurate location data for a UE. The historical location datacan be employed to determine a location of high UE population density,e.g., a dense bin. This can suggest that UEs, both reporting andnon-reporting, generally are located in the area represented by thedense bin. Historical session data can comprise information indicating asector transition(s) as a UE moves along a route, e.g., handover betweensectors during a wireless communication session. The sectortransition(s) can be associated with a location in the ending sector. Asan example, a UE that transitions sectors while traversing a route fromA to B can be associated with a first dense bin, while a UE traversingsectors on a route from B to A can be associated with a second densebin. Supplemental data can comprise data other than accurate locationdata, e.g., other than GPS data, aGPS data, etc. In an aspect,supplemental data can be employed to determine location data and, insome instances, hyper-accurate location data. Numerous examples ofsupplemental data can be readily appreciated and all are consideredwithin the scope of the current disclosure even where not explicitlyrecited herein for the sake of clarity and brevity.

At 620, method 600 can comprise correlating the historical locationdata, historical session data, and supplementary data. For a wirelessnetwork environment having a number of UEs, some of which reportaccurate location data, the correlation of historical location data,historical sector transition data, and supplemental data, can result ina data set that can suggest a relationship between directionality, as isrelated to the order of sector transitions, and a dense bin. Theinclusion of supplemental data can improve correlating a sectortransition with a dense bin and/or, in some circumstances, ahyper-accurate location. As an example, supplemental data can compriseroadway topological information, traffic information, etc., that can becorrelated to areas of high UE density as related to a historical sectortransition. Continuing the example, a correlation can be establishedreflecting that during rush hour, an area of road construction isresulting in a high density of UEs at a portion of the roadway for agiven sector transition, but that the high density does not occur atthat location outside of rush hour. Moreover, where the dense bin areais larger than the width of the roadway, the road topographysupplementary data can be correlated to the historical location andsession data to infer a hyper-accurate location, e.g., on the roadwayrather than off in the bushes along the roadway. Correlating thesupplemental information can therefore improve returned locationinferences by considering time of day, completion of the constructionevent, etc.

In an aspect, a UE historically transitioning sectors in a first ordercan be associated with the same, or a different, dense bin than a UEhistorically transitioning sectors in a second order. As such, thedirectionality or order of sector transitions can be individuallycorrelated with a dense bin that can be the same or different thancorrelations to a dense bin for other sector transitions and can reflectthe environment in which the UE is operating, e.g., route features, userpropensities, etc.

Method 600, at 630, can comprise receiving current session data. Currentsession data can comprise sector transition(s) for a current UE. Currentsession data can enable searching of correlations determined at 620,e.g., in a data set comprising a correlation(s) between the historicallocation data, historical session data, and supplementary data.Information from the current session data can be used in hashing of thedata set, a key for indexing through the data set, as a search termagainst the data set, as a filter for the data set, etc. Current sessiondata can reflect a current condition of a UE that can be used to drawconclusions from analyses performed on historical data.

At 640, method 600 can comprise inferring current location informationbased on the current session data and a correlation between thehistorical session data, historical location data, and supplementarydata. Sector transition information for the current UE can be employedto search a data set comprising a correlation(s) between historicallocation data and historical session data. As an example, where acurrent UE transitions sectors associated with a route from A to B, andthe data set comprises a correlation between historical sectortransitions on the route from A to B with a first dense bin, it can beinferred that the current UE location is within the area associated withthe first dense bin. As such, method 600 can enable access to inferredlocation data, which can be returned in response to a query as shown at650 of method 600. Inferred location data can comprise locationinformation for a first dense bin based on the current sector transitionand a correlation between the first dense bin, determined fromhistorical location data, and historical session data. At this point,method 600 can end.

FIG. 7 illustrates a method 700 that can enable access to inferredlocation data based on satisfying a distance rule related to a distancebetween a radio device location and a historically dense location withina sector in accordance with aspects of the subject disclosure. Method700 can provide location data for mobile devices in a wireless networkby leveraging sparse accurate location data. Leveraging sparse accuratelocation data can enable inferences of location for UEs. Identifying alikely location for a UE in a wireless network sector can be based onidentifying a densely populated location, e.g., a dense bin, etc., inthe sector. An inference of a UE location can be based on a locationrepresented by a dense bin of a sector. Moreover, a transition betweensectors can be employed in inferring a UE location based on acorrelation between a dense bin and a historical sector transition.

At 710, method 700 can receive historical session data, historicallocation data, and supplemental data. Historical location data cancomprise accurate location data for a UE. The historical location datacan be employed to determine a location of high UE population density,e.g., a dense bin. This can suggest that UEs, both reporting andnon-reporting, generally are located in the area represented by thedense bin. Historical session data can comprise information indicating asector transition(s) as a UE moves along a route, e.g., handover betweensectors during a wireless communication session. The sectortransition(s) can be associated with a location in the ending sector. Asan example, a UE that transitions sectors while traversing a route fromA to B can be associated with a first dense bin, while a UE traversingsectors on a route from B to A can be associated with a second densebin. Supplemental data can comprise data other than accurate locationdata, e.g., other than GPS data, aGPS data, etc. In an aspect,supplemental data can be employed to determine location data and, insome instances, hyper-accurate location data.

At 720, method 700 can comprise determining a distance between a RANdevice location and a dense bin location based on a correlation betweenthe historical location data, historical session data, and supplementarydata. For a wireless network environment having a number of UEs, some ofwhich report accurate location data, the correlation of historicallocation data, historical sector transition data, and supplemental data,can result in a data set that can suggest a relationship betweendirectionality, as is related to the order of sector transitions, and adense bin. The inclusion of supplemental data can improve correlating asector transition with a dense bin and/or, in some circumstances, ahyper-accurate location. Correlating the supplemental information cantherefore improve returned location inferences. In an aspect, a UEhistorically transitioning sectors in a first order can be associatedwith the same, or a different, dense bin than a UE historicallytransitioning sectors in a second order. As such, the directionality ororder of sector transitions can be individually correlated with a densebin that can be the same or different than correlations to a dense binfor other sector transitions and can reflect the environment in whichthe UE is operating, e.g., route features, user propensities, etc. Wherea RAN radio device location is typically fixed and known, the distancebetween the RAN radio device location and a dense bin can be determined.

Method 700, at 730, can comprise receiving current session data. Inresponse to receiving the current session data, method 700 can returnthe dense bin location as a current location based on the distancedetermined at 720 being determined to satisfy a distance rule related toa selectable distance. Current session data can comprise sectortransition(s) for a current UE. Current session data can enablesearching of correlations determined at 720, e.g., in a data setcomprising a correlation(s) between the historical location data,historical session data, and supplementary data. Current session datacan reflect a current condition of a UE that can be used to drawconclusions from analyses performed on historical data. The currentsession data can therefore be employed to infer that the current UE isat a dense bin of a sector. Where the distance between the dense bin andthe radio device satisfies the distance rule, such as for example, beinggreater than the selected distance, the location of the dense bin can bereturned in response to a location query for the current UE based on theinference.

At 740, method 700 can comprise receiving the current session data andin response, method 700 can return the location of the RAN radio deviceas the current location based on the distance determined at 720 beingdetermined not to satisfy the distance rule related to the selectabledistance. Where the distance between the dense bin and the radio devicedoes not satisfy the distance rule, such as for example, being less thanthe selected distance, the location of the RAN radio device can bereturned in response to a location query for the current UE based on theinference, e.g., where the dense bin and radio device are co-located,the location of the radio device can be used, etc. At this point, method700 can end.

FIG. 8 illustrates a method 800 that, in response to receiving alocation query, can return inferred location data from searchable datathat is updated based on a correlation between historical session data,historical location data, and available supplementary data, inaccordance with aspects of the subject disclosure. Method 800 canprovide location data for mobile devices in a wireless network byleveraging sparse accurate location data. Leveraging sparse accuratelocation data can enable inferences of location for UEs. Identifying alikely location for a UE in a wireless network sector can be based onidentifying a densely populated location, e.g., a dense bin, etc., inthe sector. An inference of a UE location can therefore be based on alocation represented by a dense bin of a sector. Moreover, a transitionbetween sectors can be employed in inferring a UE location based on acorrelation between a dense bin and a historical sector transition.

At 810, method 800 can receive historical session data, historicallocation data, and supplemental data. Historical location data cancomprise accurate location data for a UE. The historical location datacan be employed to determine a location of high UE population density,e.g., a dense bin. This can suggest that UEs, both reporting andnon-reporting, generally are located in the area represented by thedense bin. Historical session data can comprise information indicating asector transition(s) as a UE moves along a route, e.g., handover betweensectors during a wireless communication session. The sectortransition(s) can be associated with a location in the ending sector.Supplemental data can comprise data other than accurate location data.In an aspect, supplemental data can be employed to determine locationdata and, in some instances, hyper-accurate location data.

At 820, method 800 can comprise determining a bin density based on acorrelation of the historical location data, historical session data,and supplementary data. For a wireless network environment having aplurality of UEs, some of which report accurate location data, thecorrelation of historical location data, historical sector transitiondata, and supplemental data, can result in a data set that can suggest arelationship between directionality, as is related to the order ofsector transitions, and a dense bin. The inclusion of supplemental datacan improve correlating a sector transition with a dense bin and/or, insome circumstances, a hyper-accurate location. Correlating thesupplemental information can therefore improve returned locationinferences by considering time of day, completion of the constructionevent, etc. Bin density can be determined, e.g., computed, etc., basedon a KDE technique, a Gaussian estimator, etc., in view of the order ofsector transitions and in view of characteristics of the supplementaldata. As an example, where supplemental data indicates reporting devicesusing a first AP at one end of a shopping mall but not using a second APat the other end of a shopping mall, the bin density can reflect thatthe more historically dense bin is the bin comprising the first AP. Inan aspect, a UE historically transitioning sectors in a first order canbe associated with the same, or a different, dense bin than a UEhistorically transitioning sectors in a second order. As such, thedirectionality or order of sector transitions can be individuallycorrelated with a dense bin that can be the same or different thancorrelations to a dense bin for other sector transitions and can reflectthe environment in which the UE is operating, e.g., route features, userpropensities, etc.

Method 800, at 830, can comprise updating searchable data, e.g., in asearchable data set, based on the bin density and the historical sectortransition information. Where a data set comprises a correlation (s)between historical location(s), sector transition(s), and supplementaldata, a feature of the correlation can be bin density(ies) for binswithin a sector as determined at 820. In some embodiments, bin densityfor bins in a sector can be visualized as a heat map. The heat map canbe combined with other information, e.g., road maps, geographical maps,network equipment maps, etc., to visualize areas of high reporting UEdensity relative to sector transitions, e.g., a first order of sectortransitions can present a different visualization than for a secondorder of sector transitions.

At 840, method 800 can comprise enabling access to inferred currentlocation information in response to receiving a query. The inferredcurrent location information can be based on searching the searchabledata updated at 830. Current session data can comprise sectortransition(s) for a current UE. Information from the current sessiondata can be used in hashing of the data set, a key for indexing through,as a search term against, as a filter for, etc., the searchable dataupdated at 830. Current session data can reflect a current condition ofa UE that can be used to draw conclusions from analyses performed onhistorical data. As such, method 800 can enable access to inferredlocation data, which can be returned in response to a query. Inferredlocation data can comprise location information for a first dense binbased on the current sector transition and a correlation between thefirst dense bin and historical session data. At this point, method 800can end.

FIG. 9 is a schematic block diagram of a computing environment 900 withwhich the disclosed subject matter can interact. The system 900comprises one or more remote component(s) 910. The remote component(s)910 can be hardware and/or software (e.g., threads, processes, computingdevices). In some embodiments, remote component(s) 910 can compriseservers, personal servers, wireless telecommunication network devices,etc. As an example, remote component(s) 910 can be location inferencecomponent 110-310, etc., a RAN radio device, etc.

The system 900 also comprises one or more local component(s) 920. Thelocal component(s) 920 can be hardware and/or software (e.g., threads,processes, computing devices). In some embodiments, local component(s)920 can comprise, for example, a UE, etc.

One possible communication between a remote component(s) 910 and a localcomponent(s) 920 can be in the form of a data packet adapted to betransmitted between two or more computer processes. Another possiblecommunication between a remote component(s) 910 and a local component(s)920 can be in the form of circuit-switched data adapted to betransmitted between two or more computer processes in radio time slots.The system 900 comprises a communication framework 940 that can beemployed to facilitate communications between the remote component(s)910 and the local component(s) 920, and can comprise an air interface,e.g., Uu interface of a UMTS network. Remote component(s) 910 can beoperably connected to one or more remote data store(s) 950, such as ahard drive, solid state drive, SIM card, device memory, etc., that canbe employed to store information on the remote component(s) 910 side ofcommunication framework 940. Similarly, local component(s) 920 can beoperably connected to one or more local data store(s) 930, that can beemployed to store information on the local component(s) 920 side ofcommunication framework 940.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 10, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that performs particulartasks and/or implement particular abstract data types.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It is noted that thememory components described herein can be either volatile memory ornonvolatile memory, or can comprise both volatile and nonvolatilememory, by way of illustration, and not limitation, volatile memory 1020(see below), non-volatile memory 1022 (see below), disk storage 1024(see below), and memory storage 1046 (see below). Further, nonvolatilememory can be included in read only memory, programmable read onlymemory, electrically programmable read only memory, electricallyerasable read only memory, or flash memory. Volatile memory can compriserandom access memory, which acts as external cache memory. By way ofillustration and not limitation, random access memory is available inmany forms such as synchronous random access memory, dynamic randomaccess memory, synchronous dynamic random access memory, double datarate synchronous dynamic random access memory, enhanced synchronousdynamic random access memory, Synchlink dynamic random access memory,and direct Rambus random access memory. Additionally, the disclosedmemory components of systems or methods herein are intended to comprise,without being limited to comprising, these and any other suitable typesof memory.

Moreover, it is noted that the disclosed subject matter can be practicedwith other computer system configurations, comprising single-processoror multiprocessor computer systems, mini-computing devices, mainframecomputers, as well as personal computers, hand-held computing devices(e.g., personal digital assistant, phone, watch, tablet computers,netbook computers, . . . ), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network; however, some if not all aspects ofthe subject disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules can be located inboth local and remote memory storage devices.

FIG. 10 illustrates a block diagram of a computing system 1000 operableto execute the disclosed systems and methods in accordance with anembodiment. Computer 1012, which can be, for example, LIC 110-310, etc.,a UE, a RAN radio device, a NodeB, an eNodeB, etc., comprises aprocessing unit 1014, a system memory 1016, and a system bus 1018.System bus 1018 couples system components comprising, but not limitedto, system memory 1016 to processing unit 1014. Processing unit 1014 canbe any of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as processing unit1014.

System bus 1018 can be any of several types of bus structure(s)comprising a memory bus or a memory controller, a peripheral bus or anexternal bus, and/or a local bus using any variety of available busarchitectures comprising, but not limited to, industrial standardarchitecture, micro-channel architecture, extended industrial standardarchitecture, intelligent drive electronics, video electronics standardsassociation local bus, peripheral component interconnect, card bus,universal serial bus, advanced graphics port, personal computer memorycard international association bus, Firewire (Institute of Electricaland Electronics Engineers 1194), and small computer systems interface.

System memory 1016 can comprise volatile memory 1020 and nonvolatilememory 1022. A basic input/output system, containing routines totransfer information between elements within computer 1012, such asduring start-up, can be stored in nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can compriseread only memory, programmable read only memory, electricallyprogrammable read only memory, electrically erasable read only memory,or flash memory. Volatile memory 1020 comprises read only memory, whichacts as external cache memory. By way of illustration and notlimitation, read only memory is available in many forms such assynchronous random access memory, dynamic read only memory, synchronousdynamic read only memory, double data rate synchronous dynamic read onlymemory, enhanced synchronous dynamic read only memory, Synchlink dynamicread only memory, Rambus direct read only memory, direct Rambus dynamicread only memory, and Rambus dynamic read only memory.

Computer 1012 can also comprise removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, disk storage 1024. Disk storage 1024 comprises, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, flash memory card, or memory stick. In addition, disk storage1024 can comprise storage media separately or in combination with otherstorage media comprising, but not limited to, an optical disk drive suchas a compact disk read only memory device, compact disk recordabledrive, compact disk rewritable drive or a digital versatile disk readonly memory. To facilitate connection of the disk storage devices 1024to system bus 1018, a removable or non-removable interface is typicallyused, such as interface 1026.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media or communications media, whichtwo terms are used herein differently from one another as follows.

Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media cancomprise, but are not limited to, read only memory, programmable readonly memory, electrically programmable read only memory, electricallyerasable read only memory, flash memory or other memory technology,compact disk read only memory, digital versatile disk or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible media which can beused to store desired information. In this regard, the term “tangible”herein as may be applied to storage, memory or computer-readable media,is to be understood to exclude only propagating intangible signals perse as a modifier and does not relinquish coverage of all standardstorage, memory or computer-readable media that are not only propagatingintangible signals per se. In an aspect, tangible media can comprisenon-transitory media wherein the term “non-transitory” herein as may beapplied to storage, memory or computer-readable media, is to beunderstood to exclude only propagating transitory signals per se as amodifier and does not relinquish coverage of all standard storage,memory or computer-readable media that are not only propagatingtransitory signals per se. Computer-readable storage media can beaccessed by one or more local or remote computing devices, e.g., viaaccess requests, queries or other data retrieval protocols, for avariety of operations with respect to the information stored by themedium. As such, for example, a computer-readable medium can compriseexecutable instructions stored thereon that, in response to execution,cause a system comprising a processor to perform operations, comprising:receiving historical data from a data store and further receivingcurrent session data, via an air interface or other wireless interfacefrom a UE to enable inferring location data of the UE.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

It can be noted that FIG. 10 describes software that acts as anintermediary between users and computer resources described in suitableoperating environment 1000. Such software comprises an operating system1028. Operating system 1028, which can be stored on disk storage 1024,acts to control and allocate resources of computer system 1012. Systemapplications 1030 take advantage of the management of resources byoperating system 1028 through program modules 1032 and program data 1034stored either in system memory 1016 or on disk storage 1024. It is to benoted that the disclosed subject matter can be implemented with variousoperating systems or combinations of operating systems.

A user can enter commands or information into computer 1012 throughinput device(s) 1036. In some embodiments, a user interface can allowentry of user preference information, etc., and can be embodied in atouch sensitive display panel, a mouse/pointer input to a graphical userinterface (GUI), a command line controlled interface, etc., allowing auser to interact with computer 1012. As an example, a UE can comprise auser interface that accepts an input that can initiate a locationinformation query to a LIC, e.g., LIC 110-310, etc. As another example,data analysis component 314 can receive via a command line interface aparameter setting input, etc., as input by a user via a user interfacethat is located either local or remote from the LIC. Input devices 1036comprise, but are not limited to, a pointing device such as a mouse,trackball, stylus, touch pad, keyboard, microphone, joystick, game pad,satellite dish, scanner, TV tuner card, digital camera, digital videocamera, web camera, cell phone, smartphone, tablet computer, etc. Theseand other input devices connect to processing unit 1014 through systembus 1018 by way of interface port(s) 1038. Interface port(s) 1038comprise, for example, a serial port, a parallel port, a game port, auniversal serial bus, an infrared port, a Bluetooth port, an IP port, ora logical port associated with a wireless service, etc. Output device(s)1040 use some of the same type of ports as input device(s) 1036.

Thus, for example, a universal serial busport can be used to provideinput to computer 1012 and to output information from computer 1012 toan output device 1040. Output adapter 1042 is provided to illustratethat there are some output devices 1040 like monitors, speakers, andprinters, among other output devices 1040, which use special adapters.Output adapters 1042 comprise, by way of illustration and notlimitation, video and sound cards that provide means of connectionbetween output device 1040 and system bus 1018. It should be noted thatother devices and/or systems of devices provide both input and outputcapabilities such as remote computer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. Remote computer(s) 1044 can be a personal computer, a server, arouter, a network PC, cloud storage, a cloud service, code executing ina cloud-computing environment, a workstation, a microprocessor basedappliance, a peer device, or other common network node and the like, andtypically comprises many or all of the elements described relative tocomputer 1012. A cloud computing environment, the cloud, or othersimilar terms can refer to computing that can share processing resourcesand data to one or more computer and/or other device(s) on an as neededbasis to enable access to a shared pool of configurable computingresources that can be provisioned and released readily. Cloud computingand storage solutions can storing and/or processing data in third-partydata centers which can leverage an economy of scale and can viewaccessing computing resources via a cloud service in a manner similar toa subscribing to an electric utility to access electrical energy, atelephone utility to access telephonic services, etc.

For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected by way of communication connection 1050.Network interface 1048 encompasses wire and/or wireless communicationnetworks such as local area networks and wide area networks. Local areanetwork technologies comprise fiber distributed data interface, copperdistributed data interface, Ethernet, Token Ring and the like. Wide areanetwork technologies comprise, but are not limited to, point-to-pointlinks, circuit-switching networks like integrated services digitalnetworks and variations thereon, packet switching networks, and digitalsubscriber lines. As noted below, wireless technologies may be used inaddition to or in place of the foregoing.

Communication connection(s) 1050 refer(s) to hardware/software employedto connect network interface 1048 to bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to network interface 1048 can comprise, for example, internaland external technologies such as modems, comprising regular telephonegrade modems, cable modems and digital subscriber line modems,integrated services digital network adapters, and Ethernet cards.

The above description of illustrated embodiments of the subjectdisclosure, comprising what is described in the Abstract, is notintended to be exhaustive or to limit the disclosed embodiments to theprecise forms disclosed. While specific embodiments and examples aredescribed herein for illustrative purposes, various modifications arepossible that are considered within the scope of such embodiments andexamples, as those skilled in the relevant art can recognize.

In this regard, while the disclosed subject matter has been described inconnection with various embodiments and corresponding Figures, whereapplicable, it is to be understood that other similar embodiments can beused or modifications and additions can be made to the describedembodiments for performing the same, similar, alternative, or substitutefunction of the disclosed subject matter without deviating therefrom.Therefore, the disclosed subject matter should not be limited to anysingle embodiment described herein, but rather should be construed inbreadth and scope in accordance with the appended claims below.

As it employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to comprising, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit, a digital signalprocessor, a field programmable gate array, a programmable logiccontroller, a complex programmable logic device, a discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Processorscan exploit nano-scale architectures such as, but not limited to,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage or enhance performance of user equipment.A processor may also be implemented as a combination of computingprocessing units.

As used in this application, the terms “component,” “system,”“platform,” “layer,” “selector,” “interface,” and the like are intendedto refer to a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration and not limitation, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). Asanother example, a component can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry, which is operated by a software or firmwareapplication executed by a processor, wherein the processor can beinternal or external to the apparatus and executes at least a part ofthe software or firmware application. As yet another example, acomponent can be an apparatus that provides specific functionalitythrough electronic components without mechanical parts, the electroniccomponents can comprise a processor therein to execute software orfirmware that confers at least in part the functionality of theelectronic components.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Further, the term “include” is intended to be employed as an open orinclusive term, rather than a closed or exclusive term. The term“include” can be substituted with the term “comprising” and is to betreated with similar scope, unless otherwise explicitly used otherwise.As an example, “a basket of fruit including an apple” is to be treatedwith the same breadth of scope as, “a basket of fruit comprising anapple.”

Moreover, terms like “user equipment (UE),” “mobile station,” “mobile,”subscriber station,” “subscriber equipment,” “access terminal,”“terminal,” “handset,” and similar terminology, refer to a wirelessdevice utilized by a subscriber or user of a wireless communicationservice to receive or convey data, control, voice, video, sound, gaming,or substantially any data-stream or signaling-stream. The foregoingterms are utilized interchangeably in the subject specification andrelated drawings. Likewise, the terms “access point,” “base station,”“Node B,” “evolved Node B,” “eNodeB,” “home Node B,” “home accesspoint,” and the like, are utilized interchangeably in the subjectapplication, and refer to a wireless network component or appliance thatserves and receives data, control, voice, video, sound, gaming, orsubstantially any data-stream or signaling-stream to and from a set ofsubscriber stations or provider enabled devices. Data and signalingstreams can comprise packetized or frame-based flows.

Additionally, the terms “core-network”, “core”, “core carrier network”,“carrier-side”, or similar terms can refer to components of atelecommunications network that typically provides some or all ofaggregation, authentication, call control and switching, charging,service invocation, or gateways. Aggregation can refer to the highestlevel of aggregation in a service provider network wherein the nextlevel in the hierarchy under the core nodes is the distribution networksand then the edge networks. UEs do not normally connect directly to thecore networks of a large service provider but can be routed to the coreby way of a switch or radio access network. Authentication can refer todeterminations regarding whether the user requesting a service from thetelecom network is authorized to do so within this network or not. Callcontrol and switching can refer determinations related to the futurecourse of a call stream across carrier equipment based on the callsignal processing. Charging can be related to the collation andprocessing of charging data generated by various network nodes. Twocommon types of charging mechanisms found in present day networks can beprepaid charging and postpaid charging. Service invocation can occurbased on some explicit action (e.g. call transfer) or implicitly (e.g.,call waiting). It is to be noted that service “execution” may or may notbe a core network functionality as third party network/nodes may takepart in actual service execution. A gateway can be present in the corenetwork to access other networks. Gateway functionality can be dependenton the type of the interface with another network.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“prosumer,” “agent,” and the like are employed interchangeablythroughout the subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components (e.g., supportedthrough artificial intelligence, as through a capacity to makeinferences based on complex mathematical formalisms), that can providesimulated vision, sound recognition and so forth.

Aspects, features, or advantages of the subject matter can be exploitedin substantially any, or any, wired, broadcast, wirelesstelecommunication, radio technology or network, or combinations thereof.Non-limiting examples of such technologies or networks comprisebroadcast technologies (e.g., sub-Hertz, extremely low frequency, verylow frequency, low frequency, medium frequency, high frequency, veryhigh frequency, ultra-high frequency, super-high frequency, terahertzbroadcasts, etc.); Ethernet; X.25; powerline-type networking, e.g.,Powerline audio video Ethernet, etc.; femtocell technology; Wi-Fi;worldwide interoperability for microwave access; enhanced general packetradio service; third generation partnership project, long termevolution; third generation partnership project universal mobiletelecommunications system; third generation partnership project 2, ultramobile broadband; high speed packet access; high speed downlink packetaccess; high speed uplink packet access; enhanced data rates for globalsystem for mobile communication evolution radio access network;universal mobile telecommunications system terrestrial radio accessnetwork; or long term evolution advanced.

The term “infer” or “inference” can generally refer to the process ofreasoning about, or inferring states of, the system, environment, user,and/or intent from a set of observations as captured via events and/ordata. Captured data and events can include user data, device data,environment data, data from sensors, sensor data, application data,implicit data, explicit data, etc. Inference, for example, can beemployed to identify a specific context or action, or can generate aprobability distribution over states of interest based on aconsideration of data and events. Inference can also refer to techniquesemployed for composing higher-level events from a set of events and/ordata. Such inference results in the construction of new events oractions from a set of observed events and/or stored event data, whetherthe events, in some instances, can be correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. Various classification schemes and/or systems(e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, and data fusion engines) can beemployed in connection with performing automatic and/or inferred actionin connection with the disclosed subject matter.

What has been described above includes examples of systems and methodsillustrative of the disclosed subject matter. It is, of course, notpossible to describe every combination of components or methods herein.One of ordinary skill in the art may recognize that many furthercombinations and permutations of the claimed subject matter arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

What is claimed is:
 1. A system, comprising: a processor; and a memorythat stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: determiningfuture location data corresponding to a future transition of a device toa first antenna from a second antenna based on a historical distributionof devices in an area and based on historical antenna transitions of thedevices, wherein the historical distribution of the devices is based onhistorical location data for the devices in the area, wherein thehistorical antenna transitions relate to historical transitions of thedevices to the first antenna from antennas, and wherein the antennascomprise the second antenna; and in response to determining a firstoccurrence of a first transition of the device to the first antenna fromthe second antenna, enabling access to a probable location of the devicebased on the future location data.
 2. The system of claim 1, wherein thefuture location data indicates probable locations of the devicecomprising the probable location of the device, wherein the probablelocations are ordered based on a determined likelihood of the devicebeing at a future location in the area, and wherein the determinedlikelihood of the device being at the future location in the area isdetermined from the historical distribution of the devices.
 3. Thesystem of claim 1, wherein the determining the future location data isfurther based on supplementary data corresponding to the area.
 4. Thesystem of claim 3, wherein the supplementary data comprises geographicalinformation.
 5. The system of claim 3, wherein the supplementary datacomprises roadway information.
 6. The system of claim 1, wherein thefuture location data comprises an alternate future transition of thedevice to the first antenna from a third antenna, wherein the alternatefuture transition of the device is determined from the historicaldistribution of the devices in the area and the historical antennatransitions of the devices, and wherein the antennas comprise the thirdantenna.
 7. The system of claim 6, wherein, in response to determining asecond occurrence of a second transition of the device to the firstantenna from the third antenna, enabling access to an alternate probablelocation of the device based on the alternate future transitioncomprised in the future location data.
 8. The system of claim 7, whereinthe probable location of the device and the alternate probable locationof the device indicate different probable locations of the device. 9.The system of claim 7, wherein the probable location of the device andthe alternate probable location of the device indicate a same probablelocation of the device.
 10. A method, comprising: in response toreceiving, by a device comprising a processor, location probabilitydata, predicting a location of a user equipment that has transitioned toa first antenna from a different antenna based on the locationprobability data, wherein the location probability data indicates futurelocations of the user equipment ranked by a probability of the userequipment being at a future location of the future locations, whereinthe probability is determined from historical location data of userequipments and corresponding historical antenna transitions by the userequipments to the first antenna from a second antenna; and enabling, bythe device, access to location information for the location of the userequipment by a requesting device.
 11. The method of claim 10, whereinthe predicting the location of the user equipment based on the locationprobability data employs location probability data ranking the futurelocations of the user equipment by the probability of the user equipmentbeing at the future location of the future locations, wherein theprobability is further determined from additional correspondinghistorical antenna transitions by the user equipments to the firstantenna from a third antenna.
 12. The method of claim 11, wherein thepredicting the location of the user equipment based on the locationprobability data predicts a first location when the different antenna isdetermined to be the second antenna and a second location when thedifferent antenna is determined to be the third antenna.
 13. The methodof claim 12, wherein the first location and the second location aredifferent locations.
 14. The method of claim 12, wherein the firstlocation and the second location are a same location.
 15. The method ofclaim 10, wherein the predicting the location of the user equipmentbased on the location probability data employs location probability datafurther based on supplementary data corresponding to an area associatedwith the historical location data.
 16. The method of claim 15, whereinthe supplemental data comprises roadway topography informationcorresponding to the area.
 17. A machine-readable storage medium,comprising executable instructions that, when executed by a processor,facilitate performance of operations, comprising: determining futurelocation data corresponding to a future transition of a device to afirst antenna from a different antenna other than the first antennabased on a historical distribution of devices in an area and based onhistorical antenna transitions of the devices, wherein the historicaldistribution of the devices is based on historical location data for thedevices in the area, wherein the historical antenna transitions relateto historical transitions of the devices to the first antenna fromantennas, and wherein the antennas comprise a second antenna and a thirdantenna; and in response to determining the device is transitioning tothe first antenna from the different antenna, enabling access to aprobable location of the device based on the future location data. 18.The machine-readable storage medium of claim 17, wherein the probablelocation of the device is a first location where the device isdetermined to be transitioning to the first antenna from the secondantenna, wherein the probable location of the device is a secondlocation where the device is determined to be transitioning to the firstantenna from the third antenna, and wherein the first location is adifferent location than the second location.
 19. The machine-readablestorage medium of claim 17, wherein the probable location of the deviceis a first location where the device is determined to be transitioningto the first antenna from the second antenna, wherein the probablelocation of the device is a second location where the device isdetermined to be transitioning to the first antenna from the thirdantenna, and wherein the first location is a same location as the secondlocation.
 20. The machine-readable storage medium of claim 17, whereinthe determining the future location data is further based onsupplementary data, and wherein the supplementary data comprises dataselected from at least one of map data, traffic data, roadway data, oraddress data.